Prilog

Prilog

Prilog by Prilog AI converts production issues into reviewed pull requests. It ingests logs from Datadog, AWS, and more, maps incidents to GitHub or GitLab, and routes follow-ups to Jira, Linear, or G

incident-to-PRlog ingestiondevops automationincident managementgit integration
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What is Prilog?

Prilog is an AI-powered tool that converts production issues into reviewed pull requests. It ingests logs and traces from monitoring stacks like Datadog, AWS, and GCP, maps each incident to the exact code path in GitHub or GitLab, and drafts a review-ready fix. The system is designed to cut Mean Time to Repair (MTTR) by up to 90%, turning hours of debugging into minutes of review. It also routes follow-up work to task management tools like Jira, Linear, or GitHub Issues, keeping fixes within your existing workflow.

Application scenarios

  • Incident response

    Automatically generate a fix PR for recurring production issues, reducing manual debugging time.

  • Payment processing

    Detect and patch duplicate charge errors in payment APIs by correlating logs with code paths.

  • API reliability

    Fix gateway timeout spikes (e.g., 503 errors) by analyzing telemetry and deploying a reviewed patch.

  • Multi-service debugging

    Map incidents across services, dependencies, and deploys to produce a coherent fix.

  • Code review automation

    Draft PR-ready patches with full context (logs, deploys, ownership) for your team to approve.

  • Task routing

    Send unresolved issues or follow-up tasks to Jira, Linear, or GitHub Issues for backlog management.

Core Features

  • Log and trace ingestion

    Ingest recurring issue signals from Datadog, SigNoz, Honeycomb, New Relic, OpenTelemetry, AWS, GCP, and Azure.

  • Issue-to-PR engine

    Convert production issues into review-ready fixes with code context, rationale, and human-approval guardrails, using models like Prilog, OpenAI, Anthropic, and Ollama.

  • Code path mapping

    Map each incident to the exact file, function, and code path in your repository (GitHub, GitLab, Bitbucket).

  • Review-ready PR drafting

    Draft PR-ready patches into GitHub or GitLab with CI passes and human-approval workflows.

  • Task routing

    Route follow-up work to Jira, Linear, or GitHub Issues after a fix is drafted or reviewed.

  • Production Remediation Graph

    Remember how your system fails across services, dependencies, and deploys, and how your team reviews fixes, to improve future remediations.

  • Self-healing memory

    Connect incidents, repos, service dependencies, review feedback, and recurrence into a graph that learns from past fixes.

  • Context-rich alerts

    Correlate logs, deploys, and ownership to the exact file before any fix is drafted, reducing noise.

Target users

Prilog is built for engineering teams and DevOps professionals who manage production systems. It benefits site reliability engineers (SREs), backend developers, and incident responders who want to stop triaging bugs and start reviewing fixes. Teams using monitoring tools like Datadog or AWS and version control systems like GitHub or GitLab will find it most useful.

How to use Prilog?

Start by connecting your production logs and traces from Datadog, AWS, GCP, Azure, or other supported providers. Then link your code repositories (GitHub, GitLab, or Bitbucket) and task-routing tools (Jira, Linear, or GitHub Issues). Prilog will automatically ingest recurring issue signals, map them to the correct code path, draft a review-ready fix, and route follow-up work to your backlog. Your team reviews and approves the PR before deployment. You can start for free via the official website.

Pricing and free trial

The website states you can "Start for free" and offers a "Book a demo" option. No specific pricing tiers or free trial limits are mentioned.

Effect review

Prilog delivers on its promise of turning production issues into reviewed PRs, cutting MTTR by up to 90% according to the site. The integration with major monitoring and code hosting tools makes it practical for real-world stacks. The Production Remediation Graph adds a layer of intelligence that learns from past incidents, which could reduce repeat errors over time. For teams drowning in alert noise, the ability to receive a drafted fix with full context is a significant time-saver. However, the tool's effectiveness depends on your team's willingness to review and deploy AI-generated patches. Overall, it's a compelling solution for automating the most tedious part of incident response.

Frequently Asked Questions

What is Prilog?
Prilog converts production issues into reviewed pull requests by ingesting logs from Datadog, AWS, and more, mapping incidents to GitHub or GitLab, and routing follow-ups to Jira, Linear, or other tools.
Which monitoring tools does Prilog support?
Prilog supports Datadog, AWS, and other logging platforms for ingesting production issues.
How does Prilog handle code changes?
It automatically creates and reviews pull requests in GitHub or GitLab based on the incident data.
Can Prilog integrate with project management tools?
Yes, it routes follow-ups to Jira, Linear, or other similar platforms.
Does Prilog require manual setup for each incident?
No, it automates the process from log ingestion to pull request creation and follow-up routing.

Prilog - AI Tool Detail

Prilog by Prilog AI converts production issues into reviewed pull requests. It ingests logs from Datadog, AWS, and more, maps incidents to GitHub or GitLab, and routes follow-ups to Jira, Linear, or G

Category：Automation

Visit Link：https://prilog.ai/

Tags：incident-to-PR、log ingestion、devops automation、incident management、git integration